Prognostic models in obstetrics: available, but far from applicable

Co-creation of a toolkit to assist risk communication and clinical decision-making in severe preeclampsia: SPOT-Impact study design

Globally, the incidence of hypertensive disorders of pregnancy, especially preeclampsia, remains high, particularly in low- and middle-income countries. The burden of adverse maternal and perinatal outcomes is particularly high for women who develop a hypertensive disorder remote from term (<34 weeks). In parallel, many women have a suboptimal experience of care. To improve the quality of care in terms of provision and experience, there is a need to support the communication of risks and making of treatment decision in ways that promote respectful maternity care. Our study objective is to co-create a tool(kit) to support clinical decision-making, communication of risks and shared decision-making in preeclampsia with relevant stakeholders, incorporating respectful maternity care, justice, and equity principles. This qualitative study detailing the exploratory phase of co-creation takes place over 17 months (Nov 2021-March 2024) in the Greater Accra and Eastern Regions of Ghana. Informed by ethnographic observations of care interactions, in-depth interviews and focus group and group discussions, the tool(kit) will be developed with survivors and women with hypertensive disorders of pregnancy and their families, health professionals, policy makers, and researchers. The tool(kit) will consist of three components: quantitative predicted risk (based on external validated risk models or absolute risk of adverse outcomes), risk communication, and shared decision-making support. We expect to co-create a user-friendly tool(kit) to improve the quality of care for women with preeclampsia remote from term which will contribute to better maternal and perinatal health outcomes as well as better maternity care experience for women in Ghana.

The Application of a Standard Risk Threshold for the Stratification of Maternal Morbidity among Population Subgroups

OBJECTIVE

 The aim of this study was to determine if a universally applied risk score threshold for severe maternal morbidity (SMM) resulted in different performance characteristics among subgroups of the population.

STUDY DESIGN

 This is a retrospective cohort study of deliveries that occurred between July 1, 2016, and June 30, 2020, in a single health system. We examined the performance of a validated comorbidity score to stratify SMM risk in our cohort. We considered the risk score that was associated with the highest decile of predicted risk as a "screen positive" for morbidity. We then used this same threshold to calculate the sensitivity and positive predictive value (PPV) of this "highest risk" designation among subgroups of the overall cohort based on the following characteristics: age, race/ethnicity, parity, gestational age, and planned mode of delivery.

RESULTS

 In the overall cohort of 53,982 women, the C-statistic was 0.755 (95% confidence interval [CI], 0.741-0.769) and calibration plot demonstrated that the risk score was well calibrated. The model performed less well in the following groups: non-White or Hispanic (C-statistic, 0.734; 95% CI, 0.712-0.755), nulliparas (C-statistic, 0.735; 95% CI, 0.716-0.754), term deliveries (C-statistic, 0.712; 95% CI, 0.694-0.729), and planned vaginal delivery (C-statistic, 0.728; 95% CI, 0.709-0.747). There were differences in the PPVs by gestational age (7.8% term and 29.7% preterm) and by planned mode of delivery (8.7% vaginal and 17.7% cesarean delivery). Sensitivities were lower in women who were <35 years (36.6%), non-White or Hispanic (40.7%), nulliparous (38.9%), and those having a planned vaginal delivery (40.9%) than their counterparts.

CONCLUSION

 The performance of a risk score for SMM can vary by population subgroups when using standard thresholds derived from the overall cohort. If applied without such considerations, such thresholds may be less likely to identify certain subgroups of the population that may be at increased risk of SMM.

KEY POINTS

· Predictive risk models are helpful at condensing complex information into an interpretable output.. · Model performance may vary among different population subgroups.. · Prediction models should be examined for their potential to exacerbate underlying disparities..

Multivariable prediction models for fetal macrosomia and large for gestational age: A systematic review

BACKGROUND

The identification of large for gestational age (LGA) and macrosomic fetuses is essential for counselling and managing these pregnancies.

OBJECTIVES

To systematically review the literature for multivariable prediction models for LGA and macrosomia, assessing the performance, quality and applicability of the included model in clinical practice.

SEARCH STRATEGY

MEDLINE, EMBASE and Cochrane Library were searched until June 2022.

SELECTION CRITERIA

We included observational and experimental studies reporting the development and/or validation of any multivariable prediction model for fetal macrosomia and/or LGA. We excluded studies that used a single variable or did not evaluate model performance.

DATA COLLECTION AND ANALYSIS

Data were extracted using the Checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies checklist. The model performance measures discrimination, calibration and validation were extracted. The quality and completion of reporting within each study was assessed by its adherence to the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) checklist. The risk of bias and applicability were measured using PROBAST (Prediction model Risk Of Bias Assessment Tool).

MAIN RESULTS

A total of 8442 citations were identified, with 58 included in the analysis: 32/58 (55.2%) developed, 21/58 (36.2%) developed and internally validated and 2/58 (3.4%) developed and externally validated a model. Only three studies externally validated pre-existing models. Macrosomia and LGA were differentially defined by many studies. In total, 111 multivariable prediction models were developed using 112 different variables. Model discrimination was wide ranging area under the receiver operating characteristics curve (AUROC 0.56-0.96) and few studies reported calibration (11/58, 19.0%). Only 5/58 (8.6%) studies had a low risk of bias.

CONCLUSIONS

There are currently no multivariable prediction models for macrosomia/LGA that are ready for clinical implementation.

Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes

Although most pregnancies result in a good outcome, complications are not uncommon and can be associated with serious implications for mothers and babies. Predictive modeling has the potential to improve outcomes through a better understanding of risk factors, heightened surveillance for high-risk patients, and more timely and appropriate interventions, thereby helping obstetricians deliver better care. We identify and study the most important risk factors for four types of pregnancy complications: (i) severe maternal morbidity, (ii) shoulder dystocia, (iii) preterm preeclampsia, and (iv) antepartum stillbirth. We use an Explainable Boosting Machine (EBM), a high-accuracy glass-box learning method, for the prediction and identification of important risk factors. We undertake external validation and perform an extensive robustness analysis of the EBM models. EBMs match the accuracy of other black-box ML methods, such as deep neural networks and random forests, and outperform logistic regression, while being more interpretable. EBMs prove to be robust. The interpretability of the EBM models reveal surprising insights into the features contributing to risk (e.g., maternal height is the second most important feature for shoulder dystocia) and may have potential for clinical application in the prediction and prevention of serious complications in pregnancy.

Investigation of statistical methods used in prognostic prediction models for obstetric care: A 10 year-span cross-sectional study

INTRODUCTION

Obstetric care is a highly active area in the development and application of prognostic prediction models. The development and validation of these models often require the utilization of advanced statistical techniques. However, failure to adhere to rigorous methodological standards could greatly undermine the reliability and trustworthiness of the resultant models. Consequently, the aim of our study was to examine the current statistical practices employed in obstetric care and offer recommendations to enhance the utilization of statistical methods in the development of prognostic prediction models.

MATERIAL AND METHODS

We conducted a cross-sectional survey using a sample of studies developing or validating prognostic prediction models for obstetric care published in a 10-year span (2011-2020). A structured questionnaire was developed to investigate the statistical issues in five domains, including model derivation (predictor selection and algorithm development), model validation (internal and external), model performance, model presentation, and risk threshold setting. On the ground of survey results and existing guidelines, a list of recommendations for statistical methods in prognostic models was developed.

RESULTS

A total of 112 eligible studies were included, with 107 reporting model development and five exclusively reporting external validation. During model development, 58.9% of the studies did not include any form of validation. Of these, 46.4% used stepwise regression in a crude manner for predictor selection, while two-thirds made decisions on retaining or dropping candidate predictors solely based on p-values. Additionally, 26.2% transformed continuous predictors into categorical variables, and 80.4% did not consider nonlinear relationships between predictors and outcomes. Surprisingly, 94.4% of the studies did not examine the correlation between predictors. Moreover, 47.1% of the studies did not compare population characteristics between the development and external validation datasets, and only one-fifth evaluated both discrimination and calibration. Furthermore, 53.6% of the studies did not clearly present the model, and less than half established a risk threshold to define risk categories. In light of these findings, 10 recommendations were formulated to promote the appropriate use of statistical methods.

CONCLUSIONS

The use of statistical methods is not yet optimal. Ten recommendations were offered to assist the statistical methods of prognostic prediction models in obstetric care.

Clinical Application of a Graphene Oxide-Based Surface Plasmon Resonance Biosensor to Measure First-Trimester Serum Pregnancy-Associated Plasma Protein-A/A2 Ratio to Predict Preeclampsia

Background

Preeclampsia, a major cause of adverse pregnancy outcomes, involves metalloproteinases pregnancy-associated plasma protein (PAPP)-A and PAPP-A2 from placental trophoblasts. The graphene oxide (GO)-based surface plasmon resonance (SPR) biosensor has higher sensitivity, affinity, and selective ability than the traditional SPR biosensor. The aim of this study was to explore the feasibility of measuring first-trimester serum PAPP-A/PAPP-A2 ratio as a novel predictor of preeclampsia using the GO-SPR biosensor.

Methods

This prospective case-control study of pregnant women was conducted at MacKay Memorial Hospital, Taipei, Taiwan between January 2018 and June 2020. The SPR angle shifts of first-trimester serum PAPP-A, PAPP-A2, and PAPP-A/PAPP-A2 ratio measured using the GO-SPR biosensor were compared between preeclampsia and control groups.

Results

Serum samples from 185 pregnant women were collected, of whom 30 had preeclampsia (5 early-onset; 25 late-onset). The response time between the antibody-antigen association and dissociation only took about 200 seconds. The SPR angle shift of PAPP-A in the preeclampsia group was significantly smaller than that in the control group (median (interquartile range): 5.33 (4.55) versus 6.89 (4.10) millidegrees (mDeg), P = 0.008). Conversely, the SPR angle shift of PAPP-A2 in the preeclampsia group was significantly larger than that in the control group (5.70 (3.81) versus 3.63 (2.38) mDeg, P < 0.001). Receiver operating characteristic (ROC) curve analysis revealed a cut-off PAPP-A/PAPP-A2 ratio to predict all preeclampsia of ≤ 0.76, with an area under the ROC curve (AUC) of 0.79 (95% CI 0.73-0.85, P < 0.001). Sub-group analysis revealed a cut-off PAPP-A/PAPP-A2 ratio to predict early-onset preeclampsia of ≤ 0.53 (AUC 0.99, 95% CI 0.96-1.00, P < 0.001), and ≤ 0.73 to predict late-onset preeclampsia (AUC 0.75, 95% CI 0.68-0.81, P < 0.001).

Conclusion

Measuring first-trimester serum PAPP-A/PAPP-A2 ratio using the GO-SPR biosensor could be a valuable method for early prediction of preeclampsia.

Predicting adverse perinatal outcomes among gestational diabetes complicated pregnancies using neural network algorithm

OBJECTIVE

The primary aim of this study is to utilize a neural network model to predict adverse neonatal outcomes in pregnancies complicated by gestational diabetes (GDM).

DESIGN

Our model, based on XGBoost, was implemented using Python 3.6 with the Keras framework built on TensorFlow by Google. We sourced data from medical records of GDM-diagnosed individuals who delivered at our tertiary medical center between 2012 and 2016. The model included simple pregnancy parameters, maternal age, body mass index (BMI), parity, gravity, results of oral glucose tests, treatment modality, and glycemic control. The composite neonatal adverse outcomes defined as one of the following: large or small for gestational age, shoulder dystocia, fetal umbilical pH less than 7.2, neonatal intensive care unit (NICU) admission, respiratory distress syndrome (RDS), hyperbilirubinemia, or polycythemia. For the machine training phase, 70% of the cohort was randomly chosen. Each sample in this set consisted of baseline parameters and the composite outcome. The remaining samples were then employed to assess the accuracy of our model.

RESULTS

The study encompassed a total of 452 participants. The composite adverse outcome occurred in 29% of cases. Our model exhibited prediction accuracies of 82% at the time of GDM diagnosis and 91% at delivery. The factors most contributing to the prediction model were maternal age, pre-pregnancy BMI, and the results of the single 3-h 100 g oral glucose tolerance test.

CONCLUSION

Our advanced neural network algorithm has significant potential in predicting adverse neonatal outcomes in GDM-diagnosed individuals.

Prediction of fetal and neonatal outcomes after preterm manifestations of placental insufficiency: systematic review of prediction models

OBJECTIVES

To identify all prediction models for fetal and neonatal outcomes in pregnancies with preterm manifestations of placental insufficiency (gestational hypertension, pre-eclampsia, HELLP syndrome or fetal growth restriction with its onset before 37 weeks' gestation) and to assess the quality of the models and their performance on external validation.

METHODS

A systematic literature search was performed in PubMed, Web of Science and EMBASE. Studies describing prediction models for fetal/neonatal mortality or significant neonatal morbidity in patients with preterm placental insufficiency disorders were included. Data extraction was performed using the CHARMS checklist. Risk of bias was assessed using PROBAST. Literature selection and data extraction were performed by two researchers independently.

RESULTS

Our literature search yielded 22 491 unique publications. Fourteen were included after full-text screening of 218 articles that remained after initial exclusions. The studies derived a total of 41 prediction models, including four models in the setting of pre-eclampsia or HELLP, two models in the setting of fetal growth restriction and/or pre-eclampsia and 35 models in the setting of fetal growth restriction. None of the models was validated externally, and internal validation was performed in only two studies. The final models contained mainly ultrasound (Doppler) markers as predictors of fetal/neonatal mortality and neonatal morbidity. Discriminative properties were reported for 27/41 models (c-statistic between 0.6 and 0.9). Only two studies presented a calibration plot. The risk of bias was assessed as unclear in one model and high for all other models, mainly owing to the use of inappropriate statistical methods.

CONCLUSIONS

We identified 41 prediction models for fetal and neonatal outcomes in pregnancies with preterm manifestations of placental insufficiency. All models were considered to be of low methodological quality, apart from one that had unclear methodological quality. Higher-quality models and external validation studies are needed to inform clinical decision-making based on prediction models. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

On optimal timing of antenatal corticosteroids: time to reformulate the question

Administration of antenatal corticosteroids (ACS) for accelerating foetal lung maturation in threatened preterm birth is one of the cornerstones of prevention of neonatal mortality and morbidity. To identify the optimal timing of ACS administration, most studies have compared subgroups based on treatment-to-delivery intervals. Such subgroup analysis of the first placebo-controlled randomised controlled trial indicated that a one to seven day interval between ACS administration and birth resulted in the lowest rates of neonatal respiratory distress syndrome. This efficacy window was largely confirmed by a series of subgroup analyses of subsequent trials and observational studies and strongly influenced obstetric management. However, these subgroup analyses suffer from a methodological flaw that often seems to be overlooked and potentially has important consequences for drawing valid conclusions. In this commentary, we point out that studies comparing treatment outcomes between subgroups that are retrospectively identified at birth (i.e. after randomisation) may not only be plagued by post-randomisation confounding bias but, more importantly, may not adequately inform decision making before birth, when the projected duration of the interval is still unknown. We suggest two more formal interpretations of these subgroup analyses, using a counterfactual framework for causal inference, and demonstrate that each of these interpretations can be linked to a different hypothetical trial. However, given the infeasibility of these trials, we argue that none of these rescue interpretations are helpful for clinical decision making. As a result, guidelines based on these subgroup analyses may have led to suboptimal clinical practice. As an alternative to these flawed subgroup analyses, we suggest a more principled approach that clearly formulates the question about optimal timing of ACS treatment in terms of the protocol of a future randomised study. Even if this 'target trial' would never be conducted, its protocol may still provide important guidance to avoid repeating common design flaws when conducting observational 'real world' studies using statistical methods for causal inference.

Association of adverse outcomes with three-tiered risk assessment tool for obstetrical hemorrhage

BACKGROUND

Guidelines promote stratification for the risk for postpartum hemorrhage among parturients, although the evidence for the associated differential morbidity among the groups remains inconsistent among published reports.

OBJECTIVE

Using the California Maternal Quality Care Collaborative schema modified by the American College of Obstetrics and Gynecology, we compared the composite maternal hemorrhagic outcome and the composite neonatal adverse outcome among singletons who were categorized after delivery by the researchers as low-, medium-, or high-risk for postpartum hemorrhage. We hypothesized that the composite outcomes would be significantly different among the individuals in the different 3-tiered categories.

STUDY DESIGN

This was a retrospective cohort study of all singleton parturients with a gestational age of at least 14 weeks who delivered at a single site within 1 year. The composite maternal hemorrhagic outcome included any of the following: estimated blood loss ≥1000 mL, use of uterotonics (excluding prophylactic oxytocin) or Bakri balloon, surgical management of postpartum hemorrhage, blood transfusion, hysterectomy, thromboembolism, admission to the intensive care unit, or maternal death. The composite neonatal adverse outcome included Apgar score <7 at 5 minutes, birth injury, bronchopulmonary dysplasia, intraventricular hemorrhage, neonatal seizure, sepsis, ventilation > 6 hrs., brachial plexus palsy, hypoxic-ischemic encephalopathy, or neonatal death. Multivariable Poisson regression models with robust error variance were used to estimate the adjusted relative risks with 95% confidence intervals.

RESULTS

Of the 4544 deliveries in the study period, 4404 (96.7%) met the inclusion criteria, and among them, 1745 (39.6%) were categorized as low, 1376 (31.2%) as medium, and 1283 (29.1%) as high risk. Overall, 941 (21.4%) participants experienced the composite maternal hemorrhagic outcome with 285 (16.4%) of those being in the low-risk group, 319 (23.2%) in the medium-risk group, and 337 (26.3%) in the high-risk group. Among all parturients, 95.7% in the low-, 89.4% in the medium-, and 85.3% in the high-risk group neither had an estimated blood loss or a quantified blood loss ≥1000 mL nor were transfused. After multivariable adjustment and when compared with the low-risk group, there was a significantly higher risk for the composite maternal hemorrhagic outcome in the medium-risk group (adjusted relative risk, 1.23; 95% confidence interval, 1.05-1.43) and in the high-risk group (adjusted relative risk, 1.51; 95% confidence interval, 1.31-1.75). Overall, 366 newborns (8.4%) developed the composite neonatal adverse outcome with 76 (4.2%) in of those being in the low-risk group, 153 (11.3%) in the medium-risk group, and 140 (11.1%) in the high-risk group. After multivariable adjustment and when compared with the low-risk group, there were no significant differences in the composite neonatal adverse outcome in the medium- (adjusted relative risk, 1.27; 95% confidence interval, 0.97-1.68) or the high-risk group (adjusted relative risk, 1.29; 95% confidence interval, 0.98-1.68).

CONCLUSION

Although 8 of 10 parturients categorized as high risk neither had blood loss ≥1000 mL nor underwent transfusion, the risk stratification provides information regarding the composite maternal hemorrhagic outcome.

Prediction models for perineal lacerations during childbirth: A systematic review and critical appraisal

BACKGROUND

Perineal lacerations could lead to substantial morbidities for women. A reliable prediction model for perineal lacerations has the potential to guide the prevention. Although several prediction models have been developed to estimate the risk of perineal lacerations, especially third- and fourth-degree perineal lacerations, the evidence about the model quality and clinical applicability is scarce.

OBJECTIVES

To systematically review and critically appraise the existing prediction models for perineal lacerations.

METHODS

Seven databases (PubMed, Embase, The Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, SinoMed, China National Knowledge Infrastructure, and Wanfang Data) were systematically searched from inception to July 2022. Studies that developed prediction models for perineal lacerations or performed external validation of existing models were considered eligible to include in the systematic review. Two reviewers independently conducted data extraction according to the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies. The risk of bias and the applicability of the included models were assessed with the Prediction Model Risk of Bias Assessment Tool. A narrative synthesis was performed to summarize the characteristics, risk of bias, and performance of existing models.

RESULTS

Of 4345 retrieved studies, 14 studies with 22 prediction models for perineal lacerations were included. The included models mainly aimed to estimate the risk of third- and fourth-degree perineal lacerations. The top five predictors used were operative vaginal birth (72.7 %), parity/previous vaginal birth (63.6 %), race/ethnicity (59.1 %), maternal age (50.0 %), and episiotomy (40.1 %). Internal and external validation was performed in 12 (54.5 %) and seven (31.8 %) models, respectively. 13 studies (92.9 %) assessed model discrimination, with the c-index ranging from 0.636 to 0.830. Seven studies (50.0 %) evaluated the model calibration using the Hosmer-Lemeshow test, Brier score, or calibration curve. The results indicated that most of the models had fairly good calibration. All the included models were at higher risk of bias mainly due to unclear or inappropriate methods for handling missing data and continuous predictors, external validation, and model performance evaluation. Six models (27.3 %) showed low concerns about applicability.

CONCLUSIONS

The existing models for perineal lacerations were poorly validated and evaluated, among which only two have the potential for clinical use: one for women undergoing vaginal birth after cesarean delivery, and the other one for all women undergoing vaginal birth. Future studies should focus on robust external validation of existing models and the development of novel models for second-degree perineal laceration.

PROSPERO REGISTRATION NUMBER

CRD42022349786.

TWEETABLE ABSTRACT

The existing models for perineal lacerations during childbirth need external validation and updating. Tools are needed for second-degree perineal laceration.

A systematic review of prediction models for spontaneous preterm birth in singleton asymptomatic pregnant women with risk factors

Backgrounds

Spontaneous preterm birth (SPB) is a global problem. Early screening, identification, and prevention in asymptomatic pregnant women with risk factors for preterm birth can help reduce the incidence and mortality of preterm births. Therefore, this study systematically reviewed prediction models for spontaneous preterm birth, summarised the model characteristics, and appraised their quality to identify the best-performing prediction model for clinical decision-making.

Methods

PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, China Biology Medicine disc, VIP Database, and Wanfang Data were searched up to September 27, 2021. Prediction models for spontaneous preterm births in singleton asymptomatic pregnant women with risk factors were eligible for inclusion. Six independent reviewers selected the eligible studies and extracted data from the prediction models. The findings were summarised using descriptive statistics and visual plots.

Results

Twelve studies with twelve developmental models were included. Discriminative performance was reported in 11 studies, with an Area Under the Curve (AUC) ranging from 0.75 to 0.95. The AUCs of the seven models were greater than 0.85. Cervical length (CL) is the most commonly used predictor of spontaneous preterm birth. A total of 91.7% of the studies had a high risk of bias in the analysis domain, mainly because of the small sample size and lack of adjustment for overfitting.

Conclusion

The accuracy of the models for spontaneous preterm births in singleton asymptomatic women with risk factors was good. However, these models are not widely used in clinical practice because they lack replicability and transparency. Future studies should transparently report methodological details and consider more meaningful predictors with new progress in research on preterm birth.

Micronutrient supplementation interventions in preconception and pregnant women at increased risk of developing pre-eclampsia: a systematic review and meta-analysis

BACKGROUND

Pre-eclampsia can lead to maternal and neonatal complications and is a common cause of maternal mortality worldwide. This review has examined the effect of micronutrient supplementation interventions in women identified as having a greater risk of developing pre-eclampsia.

METHODS

A systematic review was performed using the PRISMA guidelines. The electronic databases MEDLINE, EMBASE and the Cochrane Central Register of Controlled trials were searched for relevant literature and eligible studies identified according to a pre-specified criteria. A meta-analysis of randomised controlled trials (RCTs) was conducted to examine the effect of micronutrient supplementation on pre-eclampsia in high-risk women.

RESULTS

Twenty RCTs were identified and supplementation included vitamin C and E (n = 7), calcium (n = 5), vitamin D (n = 3), folic acid (n = 2), magnesium (n = 1) and multiple micronutrients (n = 2). Sample size and recruitment time point varied across studies and a variety of predictive factors were used to identify participants, with a previous history of pre-eclampsia being the most common. No studies utilised a validated prediction model. There was a reduction in pre-eclampsia with calcium (risk difference, -0.15 (-0.27, -0.03, I2 = 83.4%)), and vitamin D (risk difference, -0.09 (-0.17, -0.02, I2 = 0.0%)) supplementation.

CONCLUSION

Our findings show a lower rate of pre-eclampsia with calcium and vitamin D, however, conclusions were limited by small sample sizes, methodological variability and heterogeneity between studies. Further higher quality, large-scale RCTs of calcium and vitamin D are warranted. Exploration of interventions at different time points before and during pregnancy as well as those which utilise prediction modelling methodology, would provide greater insight into the efficacy of micronutrient supplementation intervention in the prevention of pre-eclampsia in high-risk women.

Independent predictors of preeclampsia and their impact on the complication in Bulgarian study group of pregnant women

INTRODUCTION

One of the major obstetrical complications, affecting 2%-8% of all pregnancies, is preeclampsia. To predict the onset of preeclampsia, several methods have recently been put forth. The Fetal Medicine Foundation has developed combined screening that can identify the vast majority of women who will develop preeclampsia using a combination of maternal factors, obstetrical history, biochemical, and biophysical factors.

First-Trimester Screening for Miscarriage or Stillbirth-Prediction Model Based on MicroRNA Biomarkers

We evaluated the potential of cardiovascular-disease-associated microRNAs to predict in the early stages of gestation (from 10 to 13 gestational weeks) the occurrence of a miscarriage or stillbirth. The gene expressions of 29 microRNAs were studied retrospectively in peripheral venous blood samples derived from singleton Caucasian pregnancies diagnosed with miscarriage (n = 77 cases; early onset, n = 43 cases; late onset, n = 34 cases) or stillbirth (n = 24 cases; early onset, n = 13 cases; late onset, n = 8 cases; term onset, n = 3 cases) and 80 selected gestational-age-matched controls (normal term pregnancies) using real-time RT-PCR. Altered expressions of nine microRNAs (upregulation of miR-1-3p, miR-16-5p, miR-17-5p, miR-26a-5p, miR-146a-5p, and miR-181a-5p and downregulation of miR-130b-3p, miR-342-3p, and miR-574-3p) were observed in pregnancies with the occurrence of a miscarriage or stillbirth. The screening based on the combination of these nine microRNA biomarkers revealed 99.01% cases at a 10.0% false positive rate (FPR). The predictive model for miscarriage only was based on the altered gene expressions of eight microRNA biomarkers (upregulation of miR-1-3p, miR-16-5p, miR-17-5p, miR-26a-5p, miR-146a-5p, and miR-181a-5p and downregulation of miR-130b-3p and miR-195-5p). It was able to identify 80.52% cases at a 10.0% FPR. Highly efficient early identification of later occurrences of stillbirth was achieved via the combination of eleven microRNA biomarkers (upregulation of miR-1-3p, miR-16-5p, miR-17-5p, miR-20a-5p, miR-146a-5p, and miR-181a-5p and downregulation of miR-130b-3p, miR-145-5p, miR-210-3p, miR-342-3p, and miR-574-3p) or, alternatively, by the combination of just two upregulated microRNA biomarkers (miR-1-3p and miR-181a-5p). The predictive power achieved 95.83% cases at a 10.0% FPR and, alternatively, 91.67% cases at a 10.0% FPR. The models based on the combination of selected cardiovascular-disease-associated microRNAs had very high predictive potential for miscarriages or stillbirths and may be implemented in routine first-trimester screening programs.

What proportion of clinical prediction models make it to clinical practice? Protocol for a two-track follow-up study of prediction model development publications

INTRODUCTION

It is known that only a limited proportion of developed clinical prediction models (CPMs) are implemented and/or used in clinical practice. This may result in a large amount of research waste, even when considering that some CPMs may demonstrate poor performance. Cross-sectional estimates of the numbers of CPMs that have been developed, validated, evaluated for impact or utilized in practice, have been made in specific medical fields, but studies across multiple fields and studies following up the fate of CPMs are lacking.

METHODS AND ANALYSIS

We have conducted a systematic search for prediction model studies published between January 1995 and December 2020 using the Pubmed and Embase databases, applying a validated search strategy. Taking random samples for every calendar year, abstracts and articles were screened until a target of 100 CPM development studies were identified. Next, we will perform a forward citation search of the resulting CPM development article cohort to identify articles on external validation, impact assessment or implementation of those CPMs. We will also invite the authors of the development studies to complete an online survey to track implementation and clinical utilization of the CPMs.We will conduct a descriptive synthesis of the included studies, using data from the forward citation search and online survey to quantify the proportion of developed models that are validated, assessed for their impact, implemented and/or used in patient care. We will conduct time-to-event analysis using Kaplan-Meier plots.

ETHICS AND DISSEMINATION

No patient data are involved in the research. Most information will be extracted from published articles. We request written informed consent from the survey respondents. Results will be disseminated through publication in a peer-reviewed journal and presented at international conferences. OSF REGISTRATION: (https://osf.io/nj8s9).

Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges

BACKGROUND

In high-dimensional data (HDD) settings, the number of variables associated with each observation is very large. Prominent examples of HDD in biomedical research include omics data with a large number of variables such as many measurements across the genome, proteome, or metabolome, as well as electronic health records data that have large numbers of variables recorded for each patient. The statistical analysis of such data requires knowledge and experience, sometimes of complex methods adapted to the respective research questions.

METHODS

Advances in statistical methodology and machine learning methods offer new opportunities for innovative analyses of HDD, but at the same time require a deeper understanding of some fundamental statistical concepts. Topic group TG9 "High-dimensional data" of the STRATOS (STRengthening Analytical Thinking for Observational Studies) initiative provides guidance for the analysis of observational studies, addressing particular statistical challenges and opportunities for the analysis of studies involving HDD. In this overview, we discuss key aspects of HDD analysis to provide a gentle introduction for non-statisticians and for classically trained statisticians with little experience specific to HDD.

RESULTS

The paper is organized with respect to subtopics that are most relevant for the analysis of HDD, in particular initial data analysis, exploratory data analysis, multiple testing, and prediction. For each subtopic, main analytical goals in HDD settings are outlined. For each of these goals, basic explanations for some commonly used analysis methods are provided. Situations are identified where traditional statistical methods cannot, or should not, be used in the HDD setting, or where adequate analytic tools are still lacking. Many key references are provided.

CONCLUSIONS

This review aims to provide a solid statistical foundation for researchers, including statisticians and non-statisticians, who are new to research with HDD or simply want to better evaluate and understand the results of HDD analyses.

Should we use composite outcomes in obstetric clinical prediction models?

Clinical prediction models assist clinicians to estimate the natural course of a condition, and thus facilitate treatment decisions. The development of prediction models is increasingly common in obstetric research. Composite outcomes, whereby multiple outcomes are combined into a single endpoint, are frequently used in obstetric prediction models to increase statistical power when predicting rare events. Although existing literature has reviewed the positives and negatives of using composite outcomes in clinical trials, there has been minimal commentary on the implications of their use in the development and reporting of prognostic models. In this article, we review these issues, in particular, highlighting how unequal individual relationships between predictors and individual component outcomes can result in misleading conclusions, which may result in the omission of important but rare predictors or inappropriately inform clinical decisions to implement an intervention. We propose careful use, or where possible avoidance, of composite outcomes in the development of prognostic models in obstetrics. Methodological standards for developing prognostic models should be updated to standardise and appraise composite outcomes when their use is necessary. We also support previous recommendations to report on the accuracy of key components and inconsistencies among predictor variables.

Risk factors for late preterm and term stillbirth: A secondary analysis of an individual participant data meta-analysis

OBJECTIVE

Identify independent and novel risk factors for late-preterm (28-36 weeks) and term (≥37 weeks) stillbirth and explore development of a risk-prediction model.

DESIGN

Secondary analysis of an Individual Participant Data (IPD) meta-analysis investigating modifiable stillbirth risk factors.

SETTING

An IPD database from five case-control studies in New Zealand, Australia, the UK and an international online study.

POPULATION

Women with late-stillbirth (cases, n = 851), and ongoing singleton pregnancies from 28 weeks' gestation (controls, n = 2257).

METHODS

Established and novel risk factors for late-preterm and term stillbirth underwent univariable and multivariable logistic regression modelling with multiple sensitivity analyses. Variables included maternal age, body mass index (BMI), parity, mental health, cigarette smoking, second-hand smoking, antenatal-care utilisation, and detailed fetal movement and sleep variables.

MAIN OUTCOME MEASURES

Independent risk factors with adjusted odds ratios (aOR) for late-preterm and term stillbirth.

RESULTS

After model building, 575 late-stillbirth cases and 1541 controls from three contributing case-control studies were included. Risk factor estimates from separate multivariable models of late-preterm and term stillbirth were compared. As these were similar, the final model combined all late-stillbirths. The single multivariable model confirmed established demographic risk factors, but additionally showed that fetal movement changes had both increased (decreased frequency) and reduced (hiccoughs, increasing strength, frequency or vigorous fetal movements) aOR of stillbirth. Poor antenatal-care utilisation increased risk while more-than-adequate care was protective. The area-under-the-curve was 0.84 (95% CI 0.82-0.86).

CONCLUSIONS

Similarities in risk factors for late-preterm and term stillbirth suggest the same approach for risk-assessment can be applied. Detailed fetal movement assessment and inclusion of antenatal-care utilisation could be valuable in late-stillbirth risk assessment.

Systematic review and meta-analysis protocol for development and validation of a prediction model for gestational hypertension in Africa

Objective

Examining the development and validation of predictive models for gestational hypertension, evaluating the validity of the methodology, and investigating predictors typically employed in such models.

Design

Systematic review and meta-analysis protocol.

Methods

The Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) guideline will be used to carry out the study procedure. Using the key phrases "Gestational hypertension," "prediction, risk prediction," and "validation," a full systematic search will be conducted in PubMed/MEDLINE, Hinari, Cochrane Library, and Google Scholar. The methodological quality of the included studies will be evaluated using the prediction model risk of bias assessment tool. The CHARMS (checklist for critical evaluation and data extraction for systematic reviews of prediction modeling research) will be used to extract the data, and STATA 16 will be used to analyze it. The degree of study heterogeneity will be assessed using Cochrane I² statistics.

Discussion

A subgroup analysis will be performed to reduce the variance between primary studies. To examine the impact of individual studies on the pooled estimates, a sensitivity analysis will be performed. The funnel plot test and Egger's statistical test will be used to assess the small study effect. The presence of a modest study effect is shown by Egger's test (p-value 0.05), which will be handled by nonparametric trim and fill analysis using the random-effects model. The protocol has been registered in the PROSPERO-International Prospective Register of systematic reviews, with the registration number CRD42022314601.

The reporting of prognostic prediction models for obstetric care was poor: a cross-sectional survey of 10-year publications

BACKGROUND

To investigate the reporting of prognostic prediction model studies in obstetric care through a cross-sectional survey design.

METHODS

PubMed was searched to identify prognostic prediction model studies in obstetric care published from January 2011 to December 2020. The quality of reporting was assessed by the TRIPOD checklist. The overall adherence by study and the adherence by item were calculated separately, and linear regression analysis was conducted to explore the association between overall adherence and prespecified study characteristics.

RESULTS

A total of 121 studies were included, while no study completely adhered to the TRIPOD. The results showed that the overall adherence was poor (median 46.4%), and no significant improvement was observed after the release of the TRIPOD (43.9 to 46.7%). Studies including both model development and external validation had higher reporting quality versus those including model development only (68.1% vs. 44.8%). Among the 37 items required by the TRIPOD, 10 items were reported adequately with an adherence rate over of 80%, and the remaining 27 items had an adherence rate ranging from 2.5 to 79.3%. In addition, 11 items had a report rate lower than 25.0% and even covered key methodological aspects, including blinding assessment of predictors (2.5%), methods for model-building procedures (4.5%) and predictor handling (13.5%), how to use the model (13.5%), and presentation of model performance (14.4%).

CONCLUSIONS

In a 10-year span, prognostic prediction studies in obstetric care continued to be poorly reported and did not improve even after the release of the TRIPOD checklist. Substantial efforts are warranted to improve the reporting of obstetric prognostic prediction models, particularly those that adhere to the TRIPOD checklist are highly desirable.

Predicting postpartum haemorrhage: A systematic review of prognostic models

BACKGROUND

Postpartum haemorrhage (PPH) remains a leading cause of maternal mortality and morbidity worldwide, and the rate is increasing. Using a reliable predictive model could identify those at risk, support management and treatment, and improve maternal outcomes.

AIMS

To systematically identify and appraise existing prognostic models for PPH and ascertain suitability for clinical use.

MATERIALS AND METHODS

MEDLINE, CINAHL, Embase, and the Cochrane Library were searched using combinations of terms and synonyms, including 'postpartum haemorrhage', 'prognostic model', and 'risk factors'. Observational or experimental studies describing a prognostic model for risk of PPH, published in English, were included. The Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist informed data extraction and the Prediction Model Risk of Bias Assessment Tool guided analysis.

RESULTS

Sixteen studies met the inclusion criteria after screening 1612 records. All studies were hospital settings from eight different countries. Models were developed for women who experienced vaginal birth (n = 7), caesarean birth (n = 2), any type of birth (n = 2), hypertensive disorders (n = 1) and those with placental abnormalities (n = 4). All studies were at high risk of bias due to use of inappropriate analysis methods or omission of important statistical considerations or suboptimal validation.

CONCLUSIONS

No existing prognostic models for PPH are ready for clinical application. Future research is needed to externally validate existing models and potentially develop a new model that is reliable and applicable to clinical practice.

Preeclampsia prediction with blood pressure measurements: A global external validation of the ALSPAC models

OBJECTIVE

The prediction of preeclampsia in pregnancy has resulted in a plethora of prognostic models. Yet, very few make it past the development stage and most fail to influence clinical practice. The timely identification of high-risk pregnant women could deliver a tailored antenatal care regimen, particularly in low-resource settings. This study externally validated and calibrated previously published models that predicted the risk of preeclampsia, based on blood pressure (BP) at multiple time points in pregnancy, in a geographically diverse population.

METHODS

The prospective INTERBIO-21st Fetal Study included 3,391 singleton pregnancies from Brazil, Kenya, Pakistan, South Africa, Thailand and the UK, 2012-2018. Preeclampsia prediction was based on baseline characteristics, BP and deviation from the expected BP trajectory at multiple time points in pregnancy. The prediction rules from the Avon Longitudinal Study of Parents and Children (ALSPAC) were implemented in the INTERBIO-21st cohort.

RESULTS

Model discrimination was similar to the development cohort. Performance was best with baseline characteristics and a BP measurement at 34 weeks' gestation (AUC 0.85, 95 % CI 0.80-0.90). The ALSPAC models largely overestimated the true risk of preeclampsia incidence in the INTERBIO-21st cohort.

CONCLUSIONS

After recalibration, these prediction models could potentially serve as a risk stratifying tool to help identify women who might benefit from increased surveillance during pregnancy.

Development of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015

Preterm birth is a global public health problem with a significant burden on the individuals affected. The study aimed to extend current research on preterm birth prognostic model development by developing and internally validating models using machine learning classification algorithms and population-based routinely collected data in Western Australia. The longitudinal retrospective cohort study involved all births in Western Australia between 1980 and 2015, and the analytic sample contains 81,974 (8.6%) preterm births (< 37 weeks of gestation). Prediction models for preterm birth were developed using regularised logistic regression, decision trees, Random Forests, extreme gradient boosting, and multi-layer perceptron (MLP). Predictors included maternal socio-demographics and medical conditions, current and past pregnancy complications, and family history. Class weight was applied to handle imbalanced outcomes and stratified tenfold cross-validation was used to reduce overfitting. Close to half of the preterm births (49.1% at 5% FPR, 95% CI 48.9%,49.5%) were correctly classified by the best performing classifier (MLP) for all women when current pregnancy information was available. The sensitivity was boosted to 52.7% (95% CI 52.1%,53.3%) after including past obstetric history in a sub-population of births from multiparous women. Around half of the preterm birth can be identified antenatally at high specificity using population-based routinely collected maternal and pregnancy data. The performance of the prediction models depends on the available predictor pool that is individual and time specific.

Developing and validating a risk prediction model for preterm birth at Felege Hiwot Comprehensive Specialized Hospital, North-West Ethiopia: a retrospective follow-up study

OBJECTIVE

To develop and validate a risk prediction model for the prediction of preterm birth using maternal characteristics.

DESIGN

This was a retrospective follow-up study. Data were coded and entered into EpiData, V.3.02, and were analysed using R statistical programming language V.4.0.4 for further processing and analysis. Bivariable logistic regression was used to identify the relationship between each predictor and preterm birth. Variables with p≤0.25 from the bivariable analysis were entered into a backward stepwise multivariable logistic regression model, and significant variables (p<0.05) were retained in the multivariable model. Model accuracy and goodness of fit were assessed by computing the area under the receiver operating characteristic curve (discrimination) and calibration plot (calibration), respectively.

SETTING AND PARTICIPANTS

This retrospective study was conducted among 1260 pregnant women who did prenatal care and finally delivered at Felege Hiwot Comprehensive Specialised Hospital, Bahir Dar city, north-west Ethiopia, from 30 January 2019 to 30 January 2021.

RESULTS

Residence, gravidity, haemoglobin <11 mg/dL, early rupture of membranes, antepartum haemorrhage and pregnancy-induced hypertension remained in the final multivariable prediction model. The area under the curve of the model was 0.816 (95% CI 0.779 to 0.856).

CONCLUSION

This study showed the possibility of predicting preterm birth using maternal characteristics during pregnancy. Thus, use of this model could help identify pregnant women at a higher risk of having a preterm birth to be linked to a centre.

A multistate competing risks framework for preconception prediction of pregnancy outcomes

Background

Preconception pregnancy risk profiles—characterizing the likelihood that a pregnancy attempt results in a full-term birth, preterm birth, clinical pregnancy loss, or failure to conceive—can provide critical information during the early stages of a pregnancy attempt, when obstetricians are best positioned to intervene to improve the chances of successful conception and full-term live birth. Yet the task of constructing and validating risk assessment tools for this earlier intervention window is complicated by several statistical features: the final outcome of the pregnancy attempt is multinomial in nature, and it summarizes the results of two intermediate stages, conception and gestation, whose outcomes are subject to competing risks, measured on different time scales, and governed by different biological processes. In light of this complexity, existing pregnancy risk assessment tools largely focus on predicting a single adverse pregnancy outcome, and make these predictions at some later, post-conception time point.

Methods

We reframe the individual pregnancy attempt as a multistate model comprised of two nested multinomial prediction tasks: one corresponding to conception and the other to the subsequent outcome of that pregnancy. We discuss the estimation of this model in the presence of multiple stages of outcome missingness and then introduce an inverse-probability-weighted Hypervolume Under the Manifold statistic to validate the resulting multivariate risk scores. Finally, we use data from the Effects of Aspirin in Gestation and Reproduction (EAGeR) trial to illustrate how this multistate competing risks framework might be utilized in practice to construct and validate a preconception pregnancy risk assessment tool.

Results

In the EAGeR study population, the resulting risk profiles are able to meaningfully discriminate between the four pregnancy attempt outcomes of interest and represent a significant improvement over classification by random chance.

Conclusions

As illustrated in our analysis of the EAGeR data, our proposed prediction framework expands the pregnancy risk assessment task in two key ways—by considering a broader array of pregnancy outcomes and by providing the predictions at an earlier, preconception intervention window—providing obstetricians and their patients with more information and opportunities to successfully guide pregnancy attempts.

Longitudinally Tracking Maternal Autonomic Modulation During Normal Pregnancy With Comprehensive Heart Rate Variability Analyses

Changes in the maternal autonomic nervous system are essential in facilitating the physiological changes that pregnancy necessitates. Insufficient autonomic adaptation is linked to complications such as hypertensive diseases of pregnancy. Consequently, tracking autonomic modulation during progressing pregnancy could allow for the early detection of emerging deteriorations in maternal health. Autonomic modulation can be longitudinally and unobtrusively monitored by assessing heart rate variability (HRV). Yet, changes in maternal HRV (mHRV) throughout pregnancy remain poorly understood. In previous studies, mHRV is typically assessed only once per trimester with standard HRV features. However, since gestational changes are complex and dynamic, assessing mHRV comprehensively and more frequently may better showcase the changing autonomic modulation over pregnancy. Subsequently, we longitudinally (median sessions = 8) assess mHRV in 29 healthy pregnancies with features that assess sympathetic and parasympathetic activity, as well as heart rate (HR) complexity, HR responsiveness and HR fragmentation. We find that vagal activity, HR complexity, HR responsiveness, and HR fragmentation significantly decrease. Their associated effect sizes are small, suggesting that the increasing demands of advancing gestation are well tolerated. Furthermore, we find a notable change in autonomic activity during the transition from the second to third trimester, highlighting the dynamic nature of changes in pregnancy. Lastly, while we saw the expected rise in mean HR with gestational age, we also observed increased autonomic deceleration activity, seemingly to counter this rising mean HR. These results are an important step towards gaining insights into gestational physiology as well as tracking maternal health via mHRV.

A Model to Predict Vaginal Delivery and Maternal and Neonatal Morbidity in Low-Risk Nulliparous Patients at Term

OBJECTIVE

This study aimed to develop and validate a model to predict the probability of vaginal delivery (VD) in low-risk term nulliparous patients, and to determine whether it can predict the risk of severe maternal and neonatal morbidity.

METHODS

Secondary analysis of an obstetric cohort of patients and their neonates born in 25 hospitals across the United States (n = 115,502). Trained and certified research personnel abstracted the maternal and neonatal records. Nulliparous patients with singleton, nonanomalous vertex fetuses, admitted with an intent for VD ≥ 37 weeks were included in this analysis. Patients in active labor (cervical exam > 5 cm), those with prior cesarean and other comorbidities were excluded. Eligible patients were randomly divided into a training and test sets. Based on the training set, and using factors available at the time of admission for delivery, we developed and validated a logistic regression model to predict the probability of VD, and then estimated the prevalences of severe morbidity according to the predicted probability of VD.

RESULTS

A total of 19,611 patients were included. Based on the training set (n = 9,739), a logistic regression model was developed that included maternal age, body mass index (BMI), cervical dilatation, and gestational age on admission. The model was internally validated on the test set (n = 9,872 patients) and yielded a receiver operating characteristic-area under the curve (ROC-AUC) of 0.71 (95% confidence interval [CI]: 0.70-0.72). Based on a subset of 18,803 patients with calculated predicted probabilities, we demonstrated that the prevalences of severe morbidity decreased as the predicted probability of VD increased (p < 0.01).

CONCLUSION

In a large cohort of low-risk nulliparous patients in early labor or undergoing induction of labor, at term with singleton gestations, we developed and validated a model to calculate the probability of VD, and maternal and neonatal morbidity. If externally validated, this calculator may be clinically useful in helping to direct level of care, staffing, and adjustment for case-mix among various systems.

KEY POINTS

· A model to predict the probability of vaginal delivery in low-risk nulliparous patients at term.. · The model also predicts the risk of severe maternal and neonatal morbidity.. · The prevalences of severe morbidity decrease as the probability of vaginal delivery increases..

Prediction of Emergency Cesarean Section Using Machine Learning Methods: Development and External Validation of a Nationwide Multicenter Dataset in Republic of Korea

This study was a multicenter retrospective cohort study of term nulliparous women who underwent labor, and was conducted to develop an automated machine learning model for prediction of emergent cesarean section (CS) before onset of labor. Nine machine learning methods of logistic regression, random forest, Support Vector Machine (SVM), gradient boosting, extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), k-nearest neighbors (KNN), Voting, and Stacking were applied and compared for prediction of emergent CS during active labor. External validation was performed using a nationwide multicenter dataset for Korean fetal growth. A total of 6549 term nulliparous women was included in the analysis, and the emergent CS rate was 16.1%. The C-statistics values for KNN, Voting, XGBoost, Stacking, gradient boosting, random forest, LGBM, logistic regression, and SVM were 0.6, 0.69, 0.64, 0.59, 0.66, 0.68, 0.68, 0.7, and 0.69, respectively. The logistic regression model showed the best predictive performance with an accuracy of 0.78. The machine learning model identified nine significant variables of maternal age, height, weight at pre-pregnancy, pregnancy-associated hypertension, gestational age, and fetal sonographic findings. The C-statistic value for the logistic regression machine learning model in the external validation set (1391 term nulliparous women) was 0.69, with an overall accuracy of 0.68, a specificity of 0.83, and a sensitivity of 0.41. Machine learning algorithms with clinical and sonographic parameters at near term could be useful tools to predict individual risk of emergent CS during active labor in nulliparous women.

Composite Outcomes in Clinical Prediction Modeling: Are We Trying to Predict Apples and Oranges?

Composite outcomes are widely used in clinical research. Existing literature has considered the pros and cons of composite outcomes in clinical trials, but their extensive use in clinical prediction has received much less attention. Clinical prediction assists decision-making by directing patients with higher risks of adverse outcomes toward interventions that provide the greatest benefits to those at the greatest risk. In this article, we summarize our existing understanding of the advantages and disadvantages of composite outcomes, consider how these relate to clinical prediction, and highlight the problem of key predictors having markedly different associations with individual components of the composite outcome. We suggest that a "composite outcome fallacy" may occur when a clinical prediction model is based on strong associations between key predictors and one component of a composite outcome (such as mortality) and used to direct patients toward intervention when these predictors actually have an inverse association with a more relevant component of the composite outcome (such as the use of a lifesaving intervention). We propose that clinical prediction scores using composite outcomes should report their accuracy for key components of the composite outcome and examine for inconsistencies among predictor variables.

Accuracy of approximations to recover incompletely reported logistic regression models depended on other available information

OBJECTIVE

To provide approximations to recover the full regression equation across different scenarios of incompletely reported prediction models that were developed from binary logistic regression.

STUDY DESIGN AND SETTING

In a case study, we considered four common scenarios and illustrated their corresponding approximations: (A) Missing: the intercept, Available: the regression coefficients of predictors, overall frequency of the outcome and descriptive statistics of the predictors; (B) Missing: regression coefficients and the intercept, Available: a simplified score; (C) Missing: regression coefficients and the intercept, Available: a nomogram; (D) Missing: regression coefficients and the intercept, Available: a web calculator.

RESULTS

In the scenario A, a simplified approach based on the predicted probability corresponding to the average linear predictor was inaccurate. An approximation based on the overall outcome frequency and an approximation of the linear predictor distribution was more accurate, however, the appropriateness of the underlying assumptions cannot be verified in practice. In the scenario B, the recovered equation was inaccurate due to rounding and categorization of risk scores. In the scenarios C and D, the full regression equation could be recovered with minimal error.

CONCLUSION

The accuracy of the approximations in recovering the regression equation varied depending on the available information.

Developing and Validating Nomogram to Predict Severe Postpartum Hemorrhage in Women With Placenta Previa Undergoing Cesarean Delivery: A Multicenter Retrospective Case-Control Study

Objective

Developing and validating nomogram to predict severe postpartum hemorrhage (SPPH) in women with placenta previa (PP) undergoing cesarean delivery.

Methods

We conducted a multicenter retrospective case-control study in five hospitals. In this study, 865 patients from January, 2018 to June, 2020 were enrolled in the development cohort, and 307 patients from July, 2020 to June, 2021 were enrolled in the validation cohort. Independent risk factors for SPPH were obtained by using the multivariate logistic regression, and preoperative nomogram and intraoperative nomogram were developed, respectively. We compared the discrimination, calibration, and net benefit of the two nomograms in the development cohort and validation cohort. Then, we tested whether the intraoperative nomogram could be used before operation.

Results

There were 204 patients (23.58%) in development cohort and 80 patients (26.06%) in validation cohort experienced SPPH. In development cohort, the areas under the receiver operating characteristic (ROC) curve (AUC) of the preoperative nomogram and intraoperative nomogram were 0.831 (95% CI, 0.804, 0.855) and 0.880 (95% CI, 0.854, 0.905), respectively. In validation cohort, the AUC of the preoperative nomogram and intraoperative nomogram were 0.825 (95% CI, 0.772, 0.877) and 0.853 (95% CI, 0.808, 0.898), respectively. In the validation cohort, the AUC was 0.839 (95% CI, 0.789, 0.888) when the intraoperative nomogram was used before operation.

Conclusion

We developed the preoperative nomogram and intraoperative nomogram to predict the risk of SPPH in women with PP undergoing cesarean delivery. By comparing the discrimination, calibration, and net benefit of the two nomograms in the development cohort and validation cohort, we think that the intraoperative nomogram performed better. Moreover, application of the intraoperative nomogram before operation can still achieve good prediction effect, which can be improved if the severity of placenta accreta spectrum (PAS) can be accurately distinguished preoperatively. We expect to conduct further prospective external validation studies on the intraoperative nomogram to evaluate its application value.

First trimester preeclampsia screening and prediction

Preeclampsia is a major cause of maternal and perinatal morbidity and mortality. Early-onset disease requiring preterm delivery is associated with a higher risk of complications in both mothers and babies. Evidence suggests that the administration of low-dose aspirin initiated before 16 weeks' gestation significantly reduces the rate of preterm preeclampsia. Therefore, it is important to identify pregnant women at risk of developing preeclampsia during the first trimester of pregnancy, thus allowing timely therapeutic intervention. Several professional organizations such as the American College of Obstetricians and Gynecologists (ACOG) and National Institute for Health and Care Excellence (NICE) have proposed screening for preeclampsia based on maternal risk factors. The approach recommended by ACOG and NICE essentially treats each risk factor as a separate screening test with additive detection rate and screen-positive rate. Evidence has shown that preeclampsia screening based on the NICE and ACOG approach has suboptimal performance, as the NICE recommendation only achieves detection rates of 41% and 34%, with a 10% false-positive rate, for preterm and term preeclampsia, respectively. Screening based on the 2013 ACOG recommendation can only achieve detection rates of 5% and 2% for preterm and term preeclampsia, respectively, with a 0.2% false-positive rate. Various first trimester prediction models have been developed. Most of them have not undergone or failed external validation. However, it is worthy of note that the Fetal Medicine Foundation (FMF) first trimester prediction model (namely the triple test), which consists of a combination of maternal factors and measurements of mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor, has undergone successful internal and external validation. The FMF triple test has detection rates of 90% and 75% for the prediction of early and preterm preeclampsia, respectively, with a 10% false-positive rate. Such performance of screening is superior to that of the traditional method by maternal risk factors alone. The use of the FMF prediction model, followed by the administration of low-dose aspirin, has been shown to reduce the rate of preterm preeclampsia by 62%. The number needed to screen to prevent 1 case of preterm preeclampsia by the FMF triple test is 250. The key to maintaining optimal screening performance is to establish standardized protocols for biomarker measurements and regular biomarker quality assessment, as inaccurate measurement can affect screening performance. Tools frequently used to assess quality control include the cumulative sum and target plot. Cumulative sum is a sensitive method to detect small shifts over time, and point of shift can be easily identified. Target plot is a tool to evaluate deviation from the expected multiple of median and the expected median of standard deviation. Target plot is easy to interpret and visualize. However, it is insensitive to detecting small deviations. Adherence to well-defined protocols for the measurements of mean arterial pressure, uterine artery pulsatility index, and placental growth factor is required. This article summarizes the existing literature on the different methods, recommendations by professional organizations, quality assessment of different components of risk assessment, and clinical implementation of the first trimester screening for preeclampsia.

External validation of prognostic models to predict stillbirth using International Prediction of Pregnancy Complications (IPPIC) Network database: individual participant data meta-analysis

OBJECTIVE

Stillbirth is a potentially preventable complication of pregnancy. Identifying women at high risk of stillbirth can guide decisions on the need for closer surveillance and timing of delivery in order to prevent fetal death. Prognostic models have been developed to predict the risk of stillbirth, but none has yet been validated externally. In this study, we externally validated published prediction models for stillbirth using individual participant data (IPD) meta-analysis to assess their predictive performance.

METHODS

MEDLINE, EMBASE, DH-DATA and AMED databases were searched from inception to December 2020 to identify studies reporting stillbirth prediction models. Studies that developed or updated prediction models for stillbirth for use at any time during pregnancy were included. IPD from cohorts within the International Prediction of Pregnancy Complications (IPPIC) Network were used to validate externally the identified prediction models whose individual variables were available in the IPD. The risk of bias of the models and cohorts was assessed using the Prediction study Risk Of Bias ASsessment Tool (PROBAST). The discriminative performance of the models was evaluated using the C-statistic, and calibration was assessed using calibration plots, calibration slope and calibration-in-the-large. Performance measures were estimated separately in each cohort, as well as summarized across cohorts using random-effects meta-analysis. Clinical utility was assessed using net benefit.

RESULTS

Seventeen studies reporting the development of 40 prognostic models for stillbirth were identified. None of the models had been previously validated externally, and the full model equation was reported for only one-fifth (20%, 8/40) of the models. External validation was possible for three of these models, using IPD from 19 cohorts (491 201 pregnant women) within the IPPIC Network database. Based on evaluation of the model development studies, all three models had an overall high risk of bias, according to PROBAST. In the IPD meta-analysis, the models had summary C-statistics ranging from 0.53 to 0.65 and summary calibration slopes ranging from 0.40 to 0.88, with risk predictions that were generally too extreme compared with the observed risks. The models had little to no clinical utility, as assessed by net benefit. However, there remained uncertainty in the performance of some models due to small available sample sizes.

CONCLUSIONS

The three validated stillbirth prediction models showed generally poor and uncertain predictive performance in new data, with limited evidence to support their clinical application. The findings suggest methodological shortcomings in their development, including overfitting. Further research is needed to further validate these and other models, identify stronger prognostic factors and develop more robust prediction models. © 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Improving postpartum hemorrhage risk prediction using longitudinal electronic medical records

OBJECTIVE

Postpartum hemorrhage (PPH) remains a leading cause of preventable maternal mortality in the United States. We sought to develop a novel risk assessment tool and compare its accuracy to tools used in current practice.

MATERIALS AND METHODS

We used a PPH digital phenotype that we developed and validated previously to identify 6639 PPH deliveries from our delivery cohort (N = 70 948). Using a vast array of known and potential risk factors extracted from electronic medical records available prior to delivery, we trained a gradient boosting model in a subset of our cohort. In a held-out test sample, we compared performance of our model with 3 clinical risk-assessment tools and 1 previously published model.

RESULTS

Our 24-feature model achieved an area under the receiver-operating characteristic curve (AUROC) of 0.71 (95% confidence interval [CI], 0.69-0.72), higher than all other tools (research-based AUROC, 0.67 [95% CI, 0.66-0.69]; clinical AUROCs, 0.55 [95% CI, 0.54-0.56] to 0.61 [95% CI, 0.59-0.62]). Five features were novel, including red blood cell indices and infection markers measured upon admission. Additionally, we identified inflection points for vital signs and labs where risk rose substantially. Most notably, patients with median intrapartum systolic blood pressure above 132 mm Hg had an 11% (95% CI, 8%-13%) median increase in relative risk for PPH.

CONCLUSIONS

We developed a novel approach for predicting PPH and identified clinical feature thresholds that can guide intrapartum monitoring for PPH risk. These results suggest that our model is an excellent candidate for prospective evaluation and could ultimately reduce PPH morbidity and mortality through early detection and prevention.

Prediction of pre-eclampsia

Pre-eclampsia is a leading cause of neonatal and maternal mortality and morbidity that complicates approximately 2-8% of all pregnancies worldwide. The precise cause of pre-eclampsia is not completely understood, with several environmental, genetic, and maternal factors involved in its pathogenesis and pathophysiology. An accurate predictor of pre-eclampsia will facilitate early recognition, close surveillance according to the individual risk and early intervention, and reduce the negative consequences of the disorder. Current evidence shows that no single test predicts pre-eclampsia with sufficient accuracy to be clinically useful. A combination of markers into multiparametric models may provide a more useful and feasible predictive tool for pre-eclampsia screening in the routine care setting than a test of either component alone. This review presents a summary of the current advances on prediction of pre-eclampsia, highlighting their performance and applicability. Key priorities when conducting research on predicting pre-eclampsia are also analyzed.

Prognostic model for predicting survival in very preterm infants: an external validation study

OBJECTIVE

To perform a temporal and geographical validation of a prognostic model, considered of highest methodological quality in a recently published systematic review, for predicting survival in very preterm infants admitted to the neonatal intensive care unit. The original model was developed in the UK and included gestational age, birthweight and gender.

DESIGN

External validation study in a population-based cohort.

SETTING

Dutch neonatal wards.

POPULATION OR SAMPLE

All admitted white, singleton infants born between 23⁺⁰ and 32⁺⁶  weeks of gestation between 1 January 2015 and 31 December 2019. Additionally, the model's performance was assessed in four populations of admitted infants born between 24⁺⁰ and 31⁺⁶  weeks of gestation: white singletons, non-white singletons, all singletons and all multiples.

METHODS

The original model was applied in all five validation sets. Model performance was assessed in terms of calibration and discrimination and, if indicated, it was updated.

MAIN OUTCOME MEASURES

Calibration (calibration-in-the-large and calibration slope) and discrimination (c statistic).

RESULTS

Out of 6092 infants, 5659 (92.9%) survived. The model showed good external validity as indicated by good discrimination (c statistic 0.82, 95% CI 0.79-0.84) and calibration (calibration-in-the-large 0.003, calibration slope 0.92, 95% CI 0.84-1.00). The model also showed good external validity in the other singleton populations, but required a small intercept update in the multiples population.

CONCLUSIONS

A high-quality prognostic model predicting survival in very preterm infants had good external validity in an independent, nationwide cohort. The accurate performance of the model indicates that after impact assessment, implementation of the model in clinical practice in the neonatal intensive care unit could be considered.

TWEETABLE ABSTRACT

A high-quality model predicting survival in very preterm infants is externally valid in an independent cohort.

Development and Validation of an Obstetric Comorbidity Risk Score for Clinical Use

Background: A comorbidity summary score may support early and systematic identification of women at high risk for adverse obstetric outcomes. The objective of this study was to conduct the initial development and validation of an obstetrics comorbidity risk score for automated implementation in the electronic health record (EHR) for clinical use. Methods: The score was developed and validated using EHR data for a retrospective cohort of pregnancies with delivery between 2010 and 2018 at Kaiser Permanente Northern California, an integrated health care system. The outcome used for model development consisted of adverse obstetric events from delivery hospitalization (e.g., eclampsia, hemorrhage, death). Candidate predictors included maternal age, parity, multiple gestation, and any maternal diagnoses assigned in health care encounters in the 12 months before admission for delivery. We used penalized regression for variable selection, logistic regression to fit the model, and internal validation for model evaluation. We also evaluated prenatal model performance at 18 weeks of pregnancy. Results: The development cohort (n = 227,405 pregnancies) had an outcome rate of 3.8% and the validation cohort (n = 41,683) had an outcome rate of 2.9%. Of 276 candidate predictors, 37 were included in the final model. The final model had a validation c-statistic of 0.72 (95% confidence interval [CI] 0.70-0.73). When evaluated at 18 weeks of pregnancy, discrimination was modestly diminished (c-statistic 0.68 [95% CI 0.67-0.70]). Conclusions: The obstetric comorbidity score demonstrated good discrimination for adverse obstetric outcomes. After additional appropriate validation, the score can be automated in the EHR to support early identification of high-risk women and assist efforts to ensure risk-appropriate maternal care.

Patient and provider perspectives on preterm birth risk assessment and communication

OBJECTIVE

To describe and compare how obstetric patients and care providers view preterm birth risk assessment and communication.

METHODS

We conducted eight focus groups with obstetric patients (n = 35) and 16 qualitative interviews with obstetric providers. Grounded theory was used to identify and analyze themes.

RESULTS

Patients' knowledge about preterm birth varied greatly. Similar benefits and risks of preterm birth risk counseling were discussed by patients and providers with notable exceptions: patients cited preparedness as a benefit and providers cited maternal blame, patient alienation, and estimate uncertainty as potential risks. Most patients expressed a desire to know their personalized preterm birth risk during pregnancy. Providers differed in whether they offer universal versus selective, and quantitative versus qualitative, preterm birth risk counseling. Many providers expressed concern about discussing social and structural risk factors for preterm birth.

CONCLUSION

While many patients desired knowing their personalized preterm birth risk, prenatal care providers' disclosure practices vary because of uncertainty of estimates, concerns about negative consequences and challenges of addressing systemic inequities and social determinants of health.

PRACTICE IMPLICATIONS

Given the existing asymmetry of information about preterm birth risk, providers should consider patient preferences regarding and potential benefits and risks of such disclosure in their practice.

Validation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: an individual participant data meta-analysis

BACKGROUND

Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk is needed to plan management.

OBJECTIVES

To assess the performance of existing pre-eclampsia prediction models and to develop and validate models for pre-eclampsia using individual participant data meta-analysis. We also estimated the prognostic value of individual markers.

DESIGN

This was an individual participant data meta-analysis of cohort studies.

SETTING

Source data from secondary and tertiary care.

PREDICTORS

We identified predictors from systematic reviews, and prioritised for importance in an international survey.

PRIMARY OUTCOMES

Early-onset (delivery at < 34 weeks' gestation), late-onset (delivery at ≥ 34 weeks' gestation) and any-onset pre-eclampsia.

ANALYSIS

We externally validated existing prediction models in UK cohorts and reported their performance in terms of discrimination and calibration. We developed and validated 12 new models based on clinical characteristics, clinical characteristics and biochemical markers, and clinical characteristics and ultrasound markers in the first and second trimesters. We summarised the data set-specific performance of each model using a random-effects meta-analysis. Discrimination was considered promising for C-statistics of ≥ 0.7, and calibration was considered good if the slope was near 1 and calibration-in-the-large was near 0. Heterogeneity was quantified using I ² and τ². A decision curve analysis was undertaken to determine the clinical utility (net benefit) of the models. We reported the unadjusted prognostic value of individual predictors for pre-eclampsia as odds ratios with 95% confidence and prediction intervals.

RESULTS

The International Prediction of Pregnancy Complications network comprised 78 studies (3,570,993 singleton pregnancies) identified from systematic reviews of tests to predict pre-eclampsia. Twenty-four of the 131 published prediction models could be validated in 11 UK cohorts. Summary C-statistics were between 0.6 and 0.7 for most models, and calibration was generally poor owing to large between-study heterogeneity, suggesting model overfitting. The clinical utility of the models varied between showing net harm to showing minimal or no net benefit. The average discrimination for IPPIC models ranged between 0.68 and 0.83. This was highest for the second-trimester clinical characteristics and biochemical markers model to predict early-onset pre-eclampsia, and lowest for the first-trimester clinical characteristics models to predict any pre-eclampsia. Calibration performance was heterogeneous across studies. Net benefit was observed for International Prediction of Pregnancy Complications first and second-trimester clinical characteristics and clinical characteristics and biochemical markers models predicting any pre-eclampsia, when validated in singleton nulliparous women managed in the UK NHS. History of hypertension, parity, smoking, mode of conception, placental growth factor and uterine artery pulsatility index had the strongest unadjusted associations with pre-eclampsia.

LIMITATIONS

Variations in study population characteristics, type of predictors reported, too few events in some validation cohorts and the type of measurements contributed to heterogeneity in performance of the International Prediction of Pregnancy Complications models. Some published models were not validated because model predictors were unavailable in the individual participant data.

CONCLUSION

For models that could be validated, predictive performance was generally poor across data sets. Although the International Prediction of Pregnancy Complications models show good predictive performance on average, and in the singleton nulliparous population, heterogeneity in calibration performance is likely across settings.

FUTURE WORK

Recalibration of model parameters within populations may improve calibration performance. Additional strong predictors need to be identified to improve model performance and consistency. Validation, including examination of calibration heterogeneity, is required for the models we could not validate.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42015029349.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 72. See the NIHR Journals Library website for further project information.

Contemporary Features Of Predicting The Development Of Luteal Insufficiency And Related Gestational Disorders

The goal of our study was a comprehensive assessment of obstetric, gynecological, somatic and genetic factors, contributing to high risk of insufficient luteal phase (ILP) and relating gestational disorders to the latter in relevant patients for further optimization of therapeutic and preventive measures. Material and Methods — A cohort study with a mixed cohort was carried out. The clinical material of the retrospective study was presented based on the results of analyzing 300 cases of patients with verified diagnoses of the threat of spontaneous abortion, miscarriage, and complete spontaneous abortion, who were hospitalized in the period of 2018-2020. As part of a prospective study, we analyzed 66 blood samples of women treated at the State Budgetary Healthcare Institution Simferopol Clinical Maternity Hospital No.2 in Crimea in 2020. The polymerase chain reaction method in real time mode, with the use of the developed kits, was used for CYP3A5 6986A> G polymorphism. Results — A comprehensive assessment of obstetric, gynecological, somatic and genetic factors allowed identifying the most informative prognostic markers for the risk of developing luteal phase insufficiency and related gestational disorders, including irregular menstrual cycle, cases of drug-induced abortion, preceding specific infectious diseases (chlamydia, Ureaplasma urealyticum infection), gynecological pathology (polycystic ovary syndrome), surgical interventions performed for gynecological pathology (ovarian resection and ovariectomy), as well as single nucleotide polymorphism rs776746 in the CYP3A5 gene. Conclusion — The identified prognostic criteria make it possible to identify a group of patients with a high risk of miscarriage even before the conception; such patients need more careful and systematic medical monitoring for the timely diagnosis of possible pregnancy complications. Early diagnosing of potential issues would allow clinicians to take preventive measures, along with initiating timely treatment. As a result, the percentage of reproductive losses would go down.

Risk of caesarean delivery in labour induction: a systematic review and external validation of predictive models

BACKGROUND

Despite the existence of numerous published models predicting the risk of caesarean delivery in women undergoing induction of labour (IOL), validated models are scarce.

OBJECTIVES

To systematically review and externally assess the predictive capacity of caesarean delivery risk models in women undergoing IOL.

SEARCH STRATEGY

Studies published up to 15 January 2021 were identified through PubMed, CINAHL, Scopus and ClinicalTrials.gov, without temporal or language restrictions.

SELECTION CRITERIA

Studies describing the derivation of new models for predicting the risk of caesarean delivery in labour induction.

DATA COLLECTION AND ANALYSIS

Three authors independently screened the articles and assessed the risk of bias (ROB) according to the prediction model risk of bias assessment tool (PROBAST). External validation was performed in a prospective cohort of 468 pregnancies undergoing IOL from February 2019 to August 2020. The predictive capacity of the models was assessed by creating areas under the receiver operating characteristic curve (AUCs), calibration plots and decision curve analysis (DCA).

MAIN RESULTS

Fifteen studies met the eligibility criteria; 12 predictive models were validated. The quality of most of the included studies was not adequate. The AUC of the models varied from 0.520 to 0.773. The three models with the best discriminative capacity were those of Levine et al. (AUC 0.773, 95% CI 0.720-0.827), Hernández et al. (AUC 0.762, 95% CI 0.715-0.809) and Rossi et al. (AUC 0.752, 95% CI 0.707-0.797).

CONCLUSIONS

Predictive capacity and methodological quality were limited; therefore, we cannot currently recommend the use of any of the models for decision making in clinical practice.

TWEETABLE ABSTRACT

Predictive models that predict the risk of cesarean section in labor inductions are currently not applicable.

A prognostic model, including quantitative fetal fibronectin, to predict preterm labour: the QUIDS meta-analysis and prospective cohort study

BACKGROUND

The diagnosis of preterm labour is challenging. False-positive diagnoses are common and result in unnecessary, potentially harmful treatments (e.g. tocolytics, antenatal corticosteroids and magnesium sulphate) and costly hospital admissions. Measurement of fetal fibronectin in vaginal fluid is a biochemical test that can indicate impending preterm birth.

OBJECTIVES

To develop an externally validated prognostic model using quantitative fetal fibronectin concentration, in combination with clinical risk factors, for the prediction of spontaneous preterm birth and to assess its cost-effectiveness.

DESIGN

The study comprised (1) a qualitative study to establish the decisional needs of pregnant women and their caregivers, (2) an individual participant data meta-analysis of existing studies to develop a prognostic model for spontaneous preterm birth within 7 days in women with symptoms of preterm labour based on quantitative fetal fibronectin and clinical risk factors, (3) external validation of the prognostic model in a prospective cohort study across 26 UK centres, (4) a model-based economic evaluation comparing the prognostic model with qualitative fetal fibronectin, and quantitative fetal fibronectin with cervical length measurement, in terms of cost per QALY gained and (5) a qualitative assessment of the acceptability of quantitative fetal fibronectin.

DATA SOURCES/SETTING

The model was developed using data from five European prospective cohort studies of quantitative fetal fibronectin. The UK prospective cohort study was carried out across 26 UK centres.

PARTICIPANTS

Pregnant women at 22⁺⁰-34⁺⁶ weeks' gestation with signs and symptoms of preterm labour.

HEALTH TECHNOLOGY BEING ASSESSED

Quantitative fetal fibronectin.

MAIN OUTCOME MEASURES

Spontaneous preterm birth within 7 days.

RESULTS

The individual participant data meta-analysis included 1783 women and 139 events of spontaneous preterm birth within 7 days (event rate 7.8%). The prognostic model that was developed included quantitative fetal fibronectin, smoking, ethnicity, nulliparity and multiple pregnancy. The model was externally validated in a cohort of 2837 women, with 83 events of spontaneous preterm birth within 7 days (event rate 2.93%), an area under the curve of 0.89 (95% confidence interval 0.84 to 0.93), a calibration slope of 1.22 and a Nagelkerke R ² of 0.34. The economic analysis found that the prognostic model was cost-effective compared with using qualitative fetal fibronectin at a threshold for hospital admission and treatment of ≥ 2% risk of preterm birth within 7 days.

LIMITATIONS

The outcome proportion (spontaneous preterm birth within 7 days of test) was 2.9% in the validation study. This is in line with other studies, but having slightly fewer than 100 events is a limitation in model validation.

CONCLUSIONS

A prognostic model that included quantitative fetal fibronectin and clinical risk factors showed excellent performance in the prediction of spontaneous preterm birth within 7 days of test, was cost-effective and can be used to inform a decision support tool to help guide management decisions for women with threatened preterm labour.

FUTURE WORK

The prognostic model will be embedded in electronic maternity records and a mobile telephone application, enabling ongoing data collection for further refinement and validation of the model.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42015027590 and Current Controlled Trials ISRCTN41598423.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 52. See the NIHR Journals Library website for further project information.

Discriminant Analysis of Main Prognostic Factors Associated with Hemodynamically Significant PDA: Apgar Score, Silverman-Anderson Score, and NT-Pro-BNP Level

Hemodynamically significant patent ductus arteriosus (hsPDA) in premature newborns is associated with a risk of PDA-related morbidities. Classification into risk groups may have a clinical utility in cases of suspected hsPDA to decrease the need for echocardiograms and unnecessary treatment. This prospective observational study included 99 premature newborns with extremely low body weight, who had an echocardiogram performed within the first three days of life. Discriminant analysis was utilized to find the best combination of prognostic factors for evaluation of hsPDA. We used binary logistic regression analysis to predict the relationship between parameters and hsPDA. The cohort's mean and standard deviation gestational age was 27.6 ± 2.55 weeks, the mean birth weight was 1015 ± 274 g. Forty-six (46.4%) infants had a PDA with a mean diameter of 2.78 mm. Median NT-pro-BNP levels were 17,600 pg/mL for infants with a PDA and 2773 pg/mL in the non-hsPDA group. The combination of prognostic factors of hsPDA in newborns of extremely low body weight on the third day of life was determined: NT-pro-BNP, Apgar score, Silverman-Anderson score (Se = 82%, Sp = 88%). A cut-off value of NT-pro-BNP of more than 8500 pg/mL can predict hsPDA (Se = 84%, Sp = 86%).

The relation between second-trimester placental elasticity and poor obstetric outcomes in low-risk pregnancies

OBJECTIVES

Increased placental stiffness is associated with various pathological conditions. Our objective was to evaluate the relation between the second-trimester placental elasticity value in low-risk pregnant women and poor obstetric outcomes.

METHODS

A total of 143 pregnant women were enrolled. Placental elasticity values were measured using the transabdominal point shear wave elastography method. 10 random measurements were obtained from different areas of the placenta. The mean was accepted as the mean placental elasticity value. Logistic regression analyses were performed to identify independent variables associated with obstetric outcomes.

RESULTS

Second-trimester placental elasticity value was significantly and positively associated with the poor obstetric outcomes (p=0.038). We could predict a poor outcome with 69.2% sensitivity and 60.7% specificity if we defined the placental elasticity cut-off as 3.19 kPa. Furthermore, in the multiple regression model, the placental elasticity value added significantly to the prediction of birth weight (p=0.043).

CONCLUSIONS

Our results showed that the pregnancies with a stiffer placenta in the second trimester were associated with an increased likelihood of exhibiting poor obstetric outcomes. Also, placental elasticity was independently associated with birth weight.

Predictive models of individual risk of elective caesarean section complications: a systematic review

INTRODUCTION

With increasing caesarean section (c-section) rates, personalized communication of risk has become paramount. A reliable tool to predict complications would support evidence-based discussions around planned mode of birth. This systematic review aimed to identify, synthesize and quality appraise prognostic models of maternal complications of elective c-section.

METHODS

MEDLINE, Embase, Web of Science, CINAHL and the Cochrane Library were searched on 27 January using terms relating to 'c-section', 'prognostic models' and complications such as 'infection'. Any study developing and/or validating a prognostic model for a maternal complication of elective c-section in the English language after January 1995 was selected for analysis. Data were extracted using a predetermined checklist: source of data; participants; outcome to be predicted; candidate predictors; sample size; missing data; model development; model performance; model evaluation; results; and interpretation. Quality was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST) tool.

RESULTS

In total, 7752 studies were identified; of these, 16 full papers were reviewed and three eligible studies were identified, containing three prognostic models derived from hospitals in Japan, South Africa and the UK. The models predicted risk of blood transfusion, spinal hypotension and postpartum haemorrhage. The study authors deemed their studies to be exploratory, exploratory and confirmatory, respectively. From the three studies, a total of 29 unique candidate predictors were identified, with 15 predictors in the final models. Maternal age (n = 3), previous c-section (n = 2), placenta praevia (n = 2) and pre-operative haemoglobin (n = 2) were found to be common predictors amongst the included studies. None of the studies were externally validated and all had a high risk of bias due to the analysis technique used.

CONCLUSION

Few models have been developed to predict complications of elective c-section. Existing models predicting blood transfusion, spinal hypotension and postpartum haemorrhage cannot be recommended for clinical practice. Future research should focus on identifying predictors known before surgery and validating the resulting models.

Prognostic Models Predicting Mortality in Preterm Infants: Systematic Review and Meta-analysis

CONTEXT

Prediction models can be a valuable tool in performing risk assessment of mortality in preterm infants.

OBJECTIVE

Summarizing prognostic models for predicting mortality in very preterm infants and assessing their quality.

DATA SOURCES

Medline was searched for all articles (up to June 2020).

STUDY SELECTION

All developed or externally validated prognostic models for mortality prediction in liveborn infants born <32 weeks' gestation and/or <1500 g birth weight were included.

DATA EXTRACTION

Data were extracted by 2 independent authors. Risk of bias (ROB) and applicability assessment was performed by 2 independent authors using Prediction model Risk of Bias Assessment Tool.

RESULTS

One hundred forty-two models from 35 studies reporting on model development and 112 models from 33 studies reporting on external validation were included. ROB assessment revealed high ROB in the majority of the models, most often because of inadequate (reporting of) analysis. Internal and external validation was lacking in 41% and 96% of these models. Meta-analyses revealed an average C-statistic of 0.88 (95% confidence interval [CI]: 0.83-0.91) for the Clinical Risk Index for Babies score, 0.87 (95% CI: 0.81-0.92) for the Clinical Risk Index for Babies II score, and 0.86 (95% CI: 0.78-0.92) for the Score for Neonatal Acute Physiology Perinatal Extension II score.

LIMITATIONS

Occasionally, an external validation study was included, but not the development study, because studies developed in the presurfactant era or general NICU population were excluded.

CONCLUSIONS

Instead of developing additional mortality prediction models for preterm infants, the emphasis should be shifted toward external validation and consecutive adaption of the existing prediction models.